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Chicken infectious anemia (CIA) is a vertical transmission infectious chicken disease caused by the chicken infectious anemia virus (CAV). The disease can induce stunting and immunosuppression in chicks by infecting bone marrow-derived stem cells, causing huge economic losses for the poultry industry. To determine the prevalence of CIA in Shandong Province, China, 854 suspected CIA samples were collected and analyzed in 13 cities in Shandong from 2020 to 2022. The PCR results showed that a total of 115 CAV were isolated. The CAV-positive rates were 17.21% (26/151) in 2020, 12.23% (35/286) in 2021, and 12.94% (54/417) in 2022, with severe mixed infections. Among them, CAV and fowl adenovirus (FAdV) were the most common, accounting for 40.86%. VP1 gene homology analysis showed that isolated strains shared 96.1–100% homology with the previously reported CAV strains. Genetic variation analysis showed that most of the isolated CAV strains were located in genotype A. These results indicate that CIA infection in Shandong chickens in recent years has been prevalent and mixed infections are common, but there were no significant genetic variations. Our results extend the understanding of the prevalence and genetic evolution of CIA in Shandong Province. They will offer new references for further study of the epidemiology and virus variation and the prevention and control of this disease.

Activities, particularly harbor construction, often exert significant and non-negligible impacts on coastal environments. Therefore, it is of great practical importance to quantitatively assess the effects of such construction on the surrounding topography, such as tidal flats. This study focuses on the coast of Jiangsu Binhai Harbor. Using multi-source and multi-temporal remote sensing images, digital elevation models of tidal flats surrounding Binhai Harbor were generated for the years 2013, 2015, and 2017 through the waterline method. A quantitative analysis was conducted utilizing GIS spatial analysis techniques to examine erosion–deposition patterns, contour changes, and typical cross-sectional comparisons. The findings reveal that, although the overall coastline is in a state of erosion, the localized impacts of harbor construction are evident. Between 2013 and 2017, the northern tidal flats experienced overall erosion, whereas deposition occurred near the harbor’s root areas. Compared to 2013–2015, there was a significant decrease in erosion between 2015 and 2017, indicating that the construction of the project had a significant impact on the northern tidal flats. Throughout the five-year study period, the tidal flats within the breakwater underwent continuous adjustment, shifting from being close to the shoreline to being concentrated on both sides of the breakwater. Significant siltation was observed on the inner side of the breakwater at Binhai Harbor between 2015 and 2017, with an increase of 0.86 km2 in the area above −2 m. This study demonstrates that remote sensing technology is highly effective in monitoring changes in coastal topography, especially under the influence of human activities.

Since Neural Radiation Field (NeRF) was first proposed, a large number of studies dedicated to them have emerged. These fields achieved very good results in their respective contexts, but they are not sufficiently practical for our project. If we want to obtain novel images of satellites photographed in space by another satellite, we must face problems like inaccurate camera focal lengths and poor image texture. There are also some small structures on satellites that NeRF-like algorithms cannot render well. In these cases, the NeRF’s performance cannot sufficiently meet the project’s needs. In fact, the images rendered by the NeRF will have many incomplete structures, while the MipNeRF will blur the edges of the structures on the satellite and obtain unrealistic colors. In response to these problems, we proposed STs-NeRF, which improves the quality of the new perspective images through an encoding module and a new network structure. We found a method for calculating poses that are suitable for our dataset and that enhance the network’s input learning effect by recoding the sampling points and viewing directions through a dynamic encoding (DE) module. Then, we input them into our layer-by-layer normalized multi-layer perceptron (LLNMLP). By simultaneously inputting points and directions into the network, we avoid the mutual influence between light rays, and through layer-by-layer normalization, we ease the model’s overfitting from a training perspective. Since real images should not be made public, we created a synthetic dataset and conducted a series of experiments. The experiments showed that our method achieves the best results in reconstructing captured satellite images, compared with the NeRF, the MipNeRF, the NeuS and the NeRF2Mesh, and improves the Peak Signal-to-Noise Ratio (PSNR) by 19%. We have also tested on public datasets, and our NeRF can still render acceptable images on datasets with better textures.

It is important to achieve the 3D reconstruction of UAV remote sensing images in deep learning-based multi-view stereo (MVS) vision. The lack of obvious texture features and detailed edges in UAV remote sensing images leads to inaccurate feature point matching or depth estimation. To address this problem, this study improves the TransMVSNet algorithm in the field of 3D reconstruction by optimizing its feature extraction network and costumed body depth prediction network. The improvement is mainly achieved by extracting features with the Asymptotic Pyramidal Network (AFPN) and assigning weights to different levels of features through the ASFF module to increase the importance of key levels and also using the UNet structured network combined with an attention mechanism to predict the depth information, which also extracts the key area information. It aims to improve the performance and accuracy of the TransMVSNet algorithm’s 3D reconstruction of UAV remote sensing images. In this work, we have performed comparative experiments and quantitative evaluation with other algorithms on the DTU dataset as well as on a large UAV remote sensing image dataset. After a large number of experimental studies, it is shown that our improved TransMVSNet algorithm has better performance and robustness, providing a valuable reference for research and application in the field of 3D reconstruction of UAV remote sensing images.

A hyperspectral image (HSI) has a very high spectral resolution, which can reflect the target’s material properties well. However, the limited spatial resolution poses a constraint on its applicability. In recent years, some hyperspectral pansharpening studies have attempted to integrate HSI with PAN to improve the spatial resolution of HSI. Although some achievements have been made, there are still shortcomings, such as insufficient utilization of multi-scale spatial and spectral information, high computational complexity, and long network model inference time. To address the above issues, we propose a novel U-shaped hyperspectral pansharpening network with channel cross-concatenation and spatial–spectral attention mechanism (CCC-SSA-UNet). A novel channel cross-concatenation (CCC) method was designed to effectively enhance the fusion ability of different input source images and the fusion ability between feature maps at different levels. Regarding network design, integrating a UNet based on an encoder–decoder architecture with a spatial–spectral attention network (SSA-Net) based on residual spatial–spectral attention (Res-SSA) blocks further enhances the ability to extract spatial and spectral features. The experiment shows that our proposed CCC-SSA-UNet exhibits state-of-the-art performance and has a shorter inference runtime and lower GPU memory consumption than most of the existing hyperspectral pansharpening methods.

Premium wheat with a high end-use quality is generally lacking in China, especially high-quality hard and soft wheat. Pina-D1 and Pinb-D1 (puroindoline genes) influence wheat grain hardness (i.e., important wheat quality-related parameter) and are among the main targets in wheat breeding programs. However, the mechanism by which puroindoline genes control grain hardness remains unclear. In this study, three hard wheat puroindoline variants (MY26, GX3, and ZM1) were compared with a soft wheat variety (CM605) containing the wild-type puroindoline genotype. Specifically, proteomic methods were used to screen for differentially abundant proteins (DAPs). In total, 6253 proteins were identified and quantified via a high-throughput tandem mass tag quantitative proteomic analysis. Of the 208 DAPs, 115, 116, and 99 proteins were differentially expressed between MY26, GX3, and ZM1 (hard wheat varieties) and CM605, respectively. The cluster analysis of protein relative abundances divided the proteins into six clusters. Of these proteins, 67 and 41 proteins were, respectively, more and less abundant in CM605 than in MY26, GX3, and ZM1. Enrichment analyses detected six GO terms, five KEGG pathways, and five IPR terms that were shared by all three comparisons. Furthermore, 12 proteins associated with these terms or pathways were found to be differentially expressed in each comparison. These proteins, which included cysteine proteinase inhibitors, invertases, low-molecular-weight glutenin subunits, and alpha amylase inhibitors, may be involved in the regulation of grain hardness. The candidate genes identified in this study may be relevant for future analyses of the regulatory mechanism underlying grain hardness.

Convolutional neural networks (CNNs) have demonstrated impressive performance and have been broadly applied in hyperspectral image (HSI) classification. However, two challenging problems still exist: the first challenge is that redundant information is averse to feature learning, which damages the classification performance; the second challenge is that most of the existing classification methods only focus on single-scale feature extraction, resulting in underutilization of information. To resolve the two preceding issues, this article proposes a multiscale cross interaction attention network (MCIANet) for HSI classification. First, an interaction attention module (IAM) is designed to highlight the distinguishability of HSI and dispel redundant information. Then, a multiscale cross feature extraction module (MCFEM) is constructed to detect spectral–spatial features at different scales, convolutional layers, and branches, which can increase the diversity of spectral–spatial features. Finally, we introduce global average pooling to compress multiscale spectral–spatial features and utilize two fully connection layers, two dropout layers to obtain the output classification results. Massive experiments on three benchmark datasets demonstrate the superiority of our presented method compared with the state-of-the-art methods.

Acoustic emission (AE) is widely used in engineering and rock mechanics. The algorithm of AE location based on homogeneous medium or single velocity structure is confronted with lower accuracy when it is applied to the actual working conditions that are prevailing complicated and heterogeneous. In this paper, an AE location algorithm based on complex composite structure (CCS) is proposed via carrying out the following studies: (1) A new travel time calculation scheme suitable for CCS with step-like velocity change is proposed based on an optimized shortest path algorithm. By doing this, a reasonable ray path that is only deflected at the interface is obtained to improve the travel time accuracy. The time complexity of the new algorithm is O(nlog2n). (2) The availability of the new algorithm is verified via a theoretical analysis under a one-dimensional velocity structure as well as an AE experiment using a complex structure under artificial excitation. (3) The AE location during the failure of a simulated CCS in the laboratory indicates that the new algorithm can effectively calculate the travel time and ray path of the sample.

Satellite thermal infrared remote sensing has received worldwide attention in earthquake-precursors exploration. Meanwhile, it has also encountered great controversy due to the lack of quantitative interpretation of the observations, despite the existing qualitative physical mechanisms being able to greatly help us understand thermal infrared anomalies. Here, we report a potential mechanism to quantitatively analyze co-seismic thermal infrared anomalies based on temperature change caused by stress variation through theoretical, experimental, and field investigations. This paper firstly deduces theoretically the temperature variation during elastic deformation of rock on the basis of the thermodynamic theory. Secondly, three laboratory experiments on rock samples are conducted to verify the theoretical estimates of the temperature changes caused by stress variations using an infrared camera with the spectral range of 8~12 μm. Thirdly, a mechanical model on thrust faults is built to evaluate the co-seismic temperature drop as a result of thrust faulting. The model shows that the co-seismic temperature drop in rocks should be in the order of 0.18 K. This variation in rock temperature may cause a change in heat equivalent to changes in shallow atmospheric temperatures of 3.0–6.0 K, which is in accordance with the temperature anomalies observed by satellite thermal infrared remote sensing. In addition, the temperature change caused by crustal stress variation may involve a large spatial scale, covering the whole focal area, which has characteristics of regional distribution and is conducive to satellite observation. Therefore, a quantitative explanation of the satellite thermal infrared seismic anomaly mechanism can be given via the temperature change caused by crustal stress variation.

Recently, hyperspectral image (HSI) classification methods based on convolutional neural networks (CNN) have shown impressive performance. However, HSI classification still faces two challenging problems: the first challenge is that most existing classification approaches only focus on exploiting the fixed-scale convolutional kernels to extract spectral–spatial features, which leads to underutilization of information; the second challenge is that HSI contains a large amount of redundant information and noise, to a certain extent, which influences the classification performance of CNN. In order to tackle the above problems, this article proposes a multibranch crossover feature attention network (MCFANet) for HSI classification. The MCFANet involves two primary submodules: a cross feature extraction module (CFEM) and rearranged attention module (RAM). The former is devised to capture joint spectral–spatial features at different convolutional layers, scales and branches, which can increase the diversity and complementarity of spectral–spatial features, while the latter is constructed to spontaneously concentrate on recalibrating spatial-wise and spectral-wise feature responses, meanwhile exploit the shifted cascade operation to rearrange the obtained attention-enhanced features to dispel redundant information and noise, and thus, boost the classification performance. Compared with the state-of-the-art classification methods, massive experiments on four benchmark datasets demonstrate the meliority of our presented method.

Convolutional neural networks are widely applied in hyperspectral image (HSI) classification and show excellent performance. However, there are two challenges: the first is that fine features are generally lost in the process of depth transfer; the second is that most existing studies usually restore to first-order features, whereas they rarely consider second-order representations. To tackle the above two problems, this article proposes a hybrid-order spectral-spatial feature network (HS2FNet) for hyperspectral image classification. This framework consists of a precedent feature extraction module (PFEM) and a feature rethinking module (FRM). The former is constructed to capture multiscale spectral-spatial features and focus on adaptively recalibrate channel-wise and spatial-wise feature responses to achieve first-order spectral-spatial feature distillation. The latter is devised to heighten the representative ability of HSI by capturing the importance of feature cross-dimension, while learning more discriminative representations by exploiting the second-order statistics of HSI, thereby improving the classification performance. Massive experiments demonstrate that the proposed network achieves plausible results compared with the state-of-the-art classification methods.

Due to end-to-end optimization characteristics and fine generalization ability, convolutional neural networks have been widely applied to hyperspectral image (HSI) classification, playing an irreplaceable role. However, previous studies struggle with two major challenges: (1) HSI contains complex topographic features, the number of labeled samples in different categories is unbalanced, resulting in poor classification for categories with few labeled samples; (2) With the deepening of neural network models, it is difficult to extract more discriminative spectral-spatial features. To address the issues mentioned above, we propose a discriminative spectral-spatial-semantic feature network based on shuffle and frequency attention mechanisms for HSI classification. There are four main parts of our approach: spectral-spatial shuffle attention module (SSAM), context-aware high-level spectral-spatial feature extraction module (CHSFEM), spectral-spatial frequency attention module (SFAM), and cross-connected semantic feature extraction module (CSFEM). First, to fully excavate the category attribute information, SSAM based on a “Deconstruction-Reconstruction” structure is designed, solving the problem of poor classification performance caused by an unbalanced number of label samples. Considering that deep spectral-spatial features are difficult to extract, CHSFEM and SFAM are constructed. The former is based on the “Horizontal-Vertical” structure to capture context-aware high-level multiscale features. The latter introduces multiple frequency components to compress channels to obtain more multifarious features. Finally, towards suppressing noisy boundaries efficiently and capturing abundant semantic information, CSFEM is devised. Numerous experiments are implemented on four public datasets: the evaluation indexes of OA, AA and Kappa on four datasets all exceed 99%, demonstrating that our method can achieve satisfactory performance and is superior to other contrasting methods.

Multifarious hyperspectral image (HSI) classification methods based on convolutional neural networks (CNN) have been gradually proposed and achieve a promising classification performance. However, hyperspectral image classification still suffers from various challenges, including abundant redundant information, insufficient spectral-spatial representation, irregular class distribution, and so forth. To address these issues, we propose a novel 2D-3D CNN with spectral-spatial multi-scale feature fusion for hyperspectral image classification, which consists of two feature extraction streams, a feature fusion module as well as a classification scheme. First, we employ two diverse backbone modules for feature representation, that is, the spectral feature and the spatial feature extraction streams. The former utilizes a hierarchical feature extraction module to capture multi-scale spectral features, while the latter extracts multi-stage spatial features by introducing a multi-level fusion structure. With these network units, the category attribute information of HSI can be fully excavated. Then, to output more complete and robust information for classification, a multi-scale spectral-spatial-semantic feature fusion module is presented based on a Decomposition-Reconstruction structure. Last of all, we innovate a classification scheme to lift the classification accuracy. Experimental results on three public datasets demonstrate that the proposed method outperforms the state-of-the-art methods.

It is difficult to find correct correspondences for infrared and visible image registration because of different imaging principles. Traditional registration methods based on the point feature require designing the complicated feature descriptor and eliminate mismatched points, which results in unsatisfactory precision and much calculation time. To tackle these problems, this paper presents an artful method based on constrained point features to align infrared and visible images. The proposed method principally contains three steps. First, constrained point features are extracted by employing an object detection algorithm, which avoids constructing the complex feature descriptor and introduces the senior semantic information to improve the registration accuracy. Then, the left value rule (LV-rule) is designed to match constrained points strictly without the deletion of mismatched and redundant points. Finally, the affine transformation matrix is calculated according to matched point pairs. Moreover, this paper presents an evaluation method to automatically estimate registration accuracy. The proposed method is tested on a public dataset. Among all tested infrared-visible image pairs, registration results demonstrate that the proposed framework outperforms five state-of-the-art registration algorithms in terms of accuracy, speed, and robustness.

The effectiveness of depth information in saliency detection has been fully proved. However, it is still worth exploring how to utilize the depth information more efficiently. Erroneous depth information may cause detection failure, while non-salient objects may be closer to the camera which also leads to erroneously emphasis on non-salient regions. Moreover, most of the existing RGB-D saliency detection models have poor robustness when the salient object touches the image boundaries. To mitigate these problems, we propose a multi-stage saliency detection model with the bilateral absorbing Markov chain guided by depth information. The proposed model progressively extracts the saliency cues with three level (low-, mid-, and high-level) stages. First, we generate low-level saliency cues by explicitly combining color and depth information. Then, we design a bilateral absorbing Markov chain to calculate mid-level saliency maps. In mid-level, to suppress boundary touch problem, we present the background seed screening mechanism (BSSM) for improving the construction of the two-layer sparse graph and better selecting background-based absorbing nodes. Furthermore, the cross-modal multi-graph learning model (CMLM) is designed to fully explore the intrinsic complementary relationship between color and depth information. Finally, to obtain a more highlighted and homogeneous saliency map in high-level, we structure a depth-guided optimization module which combines cellular automata and suppression-enhancement function pair. This optimization module refines the saliency map in color space and depth space, respectively. Comprehensive experiments on three challenging benchmark datasets demonstrate the effectiveness of our proposed method both qualitatively and quantitatively.

Satellite thermal infrared remote sensing has received worldwide attention in the exploration for earthquake precursors; however, this method faces great controversy. Obtaining repeatable phenomena related to earthquakes is helpful to reduce this controversy. In this paper, a total of 15 or 17 years of Moderate-resolution Imaging Spectroradiometer (MODIS)/Aqua and MODIS/Terra satellite remote sensing land surface temperature (LST) products is selected to analyze the temperature changes before and after the Mw 7.9 earthquake in Nepal on 25 April 2015 and to explore possible thermal information associated with this earthquake. Major findings are given as follows: (1) from the time course, the temperature slowly cooled before the earthquake, reached a minimum at the time of the earthquake, and returned to normal after the earthquake. Since these changes were initiated before the earthquake, they may even have been precursors to the Nepal earthquake. (2) From the space distribution, the cooling areas correspond to the seismogenic structure during the earthquake. These cooling areas are distributed along the Himalayas and are approximately 1300 km long. The widths of the East and West sides are slightly different, with an average temperature decrease of 5.6 °C. For these cooling areas, the Western section is approximately 90 km wide and 500 km long; the East side is approximately 190 km wide and 800 km long. The Western side of the cooling strips appeared before the earthquake. In short, these kinds of spatial and temporal changes are tectonically related to the earthquake and may have been caused by the tectonic activity associated with the Nepal earthquake. This process began before the earthquake and therefore might even be potentially premonitory information associated with the Nepal earthquake.

Diffusion-based salient region detection methods have gained great popularity. In most diffusion-based methods, the saliency values are ranked on 2-layer neighborhood graph by connecting each node to its neighboring nodes and the nodes sharing common boundaries with its neighboring nodes. However, only considering the local relevance between neighbors, the salient region may be heterogeneous and even wrongly suppressed, especially when the features of salient object are diverse. In order to address the issue, we present an effective saliency detection method using diffusing process on the graph with nonlocal connections. First, a saliency-biased Gaussian model is used to refine the saliency map based on the compactness cue, and then, the saliency information of compactness is diffused on 2-layer sparse graph with nonlocal connections. Second, we obtain the contrast of each superpixel by restricting the reference region to the background. Similarly, a saliency-biased Gaussian refinement model is generated and the saliency information based on the uniqueness cue is propagated on the 2-layer sparse graph. We linearly integrate the initial saliency maps based on the compactness and uniqueness cues due to the complementarity to each other. Finally, to obtain a highlighted and homogeneous saliency map, a single-layer updating and multi-layer integrating scheme is presented. Comprehensive experiments on four benchmark datasets demonstrate that the proposed method performs better in terms of various evaluation metrics.